Sensitivity of Seasonal Climate Forecasts to Persisted SST Anomalies

Most estimates of the skill of atmospheric general circulation models (AGCMs) for forecasting seasonal climate anomalies have been based on simulations with actual observed sea surface temperatures (SSTs) as lower boundary forcing. Similarly estimates of the climatological response characteristics of AGCMs used for seasonal-to-interannual climate prediction frequently rest on historical simulations using "perfect" SST forecasts. This paper examines the errors and biases introduced into the seasonal climate response of an AGCM forced with persisted SST anomalies, which are generally considered to constitute a good prediction of SST in the first 3-month season. However, the added uncertainty introduced by the predicted SST anomalies weakens, and in some cases nullifies, the skill of atmospheric predictions that is possible given perfect SST forcing. The use of persisted SST anomalies also leads to changes in local signal-to-noise characteristics. Thus, it is argued that seasonal-to-interannual forecasts using AGCMs should be interpreted relative to historical runs that were subject to the same strategy of boundary forcing used in the current forecast in order to properly account for errors and biases introduced by the particular SST prediction strategy. Two case studies are examined to illustrate how the sensitivity of the climate response to predicted SSTs may be used as a diagnostic to suggest improvements to the predicted SSTs

Long-lead ENSO predictability from CMIP5 decadal hindcasts

Using decadal prediction experiments from the WCRP/CMIP5 suite that were initialized every year from 1960-onward, we explore long-lead predictability of ENSO events. Both deterministic and probabilistic skill metrics are used to assess the ability of these decadal prediction systems to reproduce ENSO variability as represented by the NINO3.4 index (EN3.4). Several individual systems as well as the multi-model mean can predict ENSO events 3–4 years in advance, though not for every event during the hindcast period. This long-lead skill is beyond the previously documented predictability limits of initialized prediction systems. As part of the analysis, skill in reproducing the annual cycle of EN3.4, and the annual cycle of its interannual variability is examined. Most of the prediction systems reproduce the seasonal cycle of EN3.4, but are less able to capture the timing and magnitude of the variability. However, for the prediction systems used here, the fidelity of annual cycle characteristics does not appear to be related to the system’s ability to predict ENSO events. In addition, the performance of the multi-model ensemble mean is explored and compared to the multi-model mean based solely on the most skillful systems; the latter is found to yield better results for the deterministic metrics. Finally, an analysis of the near-surface temperature and precipitation teleconnections reveals that the ability of the systems to detect ENSO events far in advance could translate into predictive skill over land for several lead years, though with reduced amplitudes compared to observations

A framework for the simulation of regional decadal variability for agricultural and other applications

Climate prediction on decadal time scales is currently an active area of research, and reliable model-based forecasts of regional "near-term" climate change have yet to be demonstrated. In the absence of such forecasts, synthetic data sequences that capture the statistical properties of observed near-term climate variability have potential value. Incorporation of a climate change component in such sequences can help define risk estimates for a range of climatic stresses, including those lying beyond what has been experienced in the past. Properly conditioned simulations can be used to drive agricultural, hydrological or other application models, enabling resilience testing of adaptation or decision systems. The use of statistically-based methods enables the efficient generation of large ensembles of synthetic sequences and consequently, the creation of well-defined probabilistic risk estimates. In this report we examine some procedures for the generation of synthetic climate sequences that incorporate both the statistics of observed variability and expectations regarding future regional climate change. Model fitting and simulation are considered in the framework of classical time series analysis, with methodology conditioned by requirements particular to the decadal climate problem. A method of downscaling annualized simulations to the daily time step, while preserving subannual statistical properties, is presented and other possible methods discussed. Deployment in the applications setting, the details of which may vary considerably, depending on regional climate characteristics, available data and the design of follow-on models, is considered and elements of a case study presented

Should teachers be expected to undertake the role of Counter-Terrorism Operatives? An investigation into the Prevent Duty Strategies requirement of UK secondary schools

Following the 7/7 London Transport Bombings, the UK Government embarked upon a policy trajectory that led to the education system being used to counter terrorism. Teachers were required to identify and report children and young people in their care if they considered them at risk of becoming radicalised and committing future terrorist attacks. This research investigates the role of UK secondary school teachers as both Government “Policy Actors” and “Counter-Terrorism Operatives”, classifying them as Counter-Terrorism “Policy Actors”. The study utilised Scheurich’s Policy Archaeology Methodology (Scheurich, 1994) and Hyatt’s Critical Policy Discourse Analysis Framework (Hyatt, 2013) to understand how this role was socially constructed within political and social contexts. This research is significant because it adds to a small but growing volume of work utilising Scheurich’s policy archaeology. Furthermore, interviews with teachers were undertaken during a global pandemic where teachers were required to undertake activities far beyond their remit. At the same time, classroom spaces had been infiltrated by geopolitics and a rise in both the far-right and Black Lives Matter movements. It questions the response of teachers in this study who believe that identifying children and young people at risk of radicalisation is an important part of their role as educators. This thesis argues that the prevailing discourse uses power, seduction, suspicion, and diversion to persuade teachers that it is within their role. The study concludes that countering terrorism is a significant undertaking and should be led by people with expertise in this field and that secondary school teachers should be allowed to focus on the subject-specific education for which they are trained

An interrogation of the selfishness paradigm in sociobiology including its explanations of altruism and a response to its interpretation of New Testament love

This thesis is a response to the sociobiological paradigm which sees all aspects of life as fundamentally 'selfish'. This view is built upon two concepts, firstly, that the evolutionary process of natural selection leads to a world characterised by 'selfish' genes and 'selfish' individual organisms. Secondly, that all aspects of human nature, including benevolence, are defined by natural selection and are consequently selfish in motivation also. In Chapter 2, the first of these ideas is shown as inappropriate, not least, because selection favours genes that 'cooperate' and individuals that 'sacrificially' expend themselves in producing offspring. In Chapter 3, the second concept is discounted as only some aspects of human behaviour and culture can be explained in terms of natural selection. These points are central to the discussions on 'altruism' in Chapters 4-6. While sociobiologists have rightly noted that kin and reciprocal forms of 'altruism' occur in nature and in human society, their rendering of them in terms of genetic and individual 'selfishness' is again entirely misleading. The arguments of some sociobiologists for group selected forms of 'altruism' in nature and human culture are shown as unconvincing. Further, the sociobiological contention that human benevolence is constrained to the aiding of kin, reciprocal partners and group members is also countered. Humans exhibit the capacity to care for those outside of these sociobiological categories. Moreover, rather than being primarily selfish in motivation, humans are both more altruistic and more egoistic than the sociobiological view can accommodate. Chapter 7 considers the sociobiological interpretation of the New Testament (NT) teachings on love as selfishly concerned only with the care of kin, reciprocators and group members. This view is largely acceded to by the theologian, Stephen Pope, while another, Patrfcia Williams, has argued that the NT directly strives to counter such innate forms of behaviour. Chapters 8-10 investigate some of the NT teachings on love and argue for a more profound and complex altruism than any of these views. Chapter 8 contends that NT love is a deeply humble and sacrificial altruism where the needs of the other are placed before those of the self; one that is patterned after the example of Christ. It is a radical altruism, which as Chapter 9 argues, encompasses kin but also goes beyond this category in the requirement to love the new family of believers. This love of the group, the church, is itself transcended in a love for all others. Chapter 10 argues that this NT altruism is not bound by reciprocity for it prioritises the care of the weak, those who cannot reciprocate; and extends love to enemies, those who will not reciprocate. The view that such a love is ultimately reciprocal on the grounds of its heavenly reward is countered, as the NT reward of love is the promise that the believer's capacity for self-giving love will be perfected.University of Chester Gladstone bursary for financial supportSt Deiniol's Library in Hawarden bursary for residential accomodation and use of librar

El Niño: Catastrophe or Opportunity

El Niño–Southern Oscillation (ENSO) phenomenon, a periodic warming of sea surface temperatures in the eastern and central equatorial Pacific, generates a significant proportion of short-term climate variations globally, second only to the seasonal cycle. Global economic losses of tens of billions of dollars are attributed to extremes of ENSO (i.e., El Niño and La Niña), suggesting that these events disproportionately trigger socioeconomic disasters on the global scale. Since global El Niño/La Niña–associated climate impacts were first documented in the 1980s, the prevailing assumption has been that more severe and widespread climate anomalies, and, therefore, greater climate-related socioeconomic losses, should be expected during ENSO extremes. Contrary to expectations, climate anomalies associated with such losses are not greater overall during ENSO extremes than during neutral periods. However, during El Niño and La Niña events climate forecasts are shown to be more accurate. Stronger ENSO events lead to greater predictability of the climate and, potentially, the socioeconomic outcomes. Thus, the prudent use of climate forecasts could mitigate adverse impacts and lead instead to increased beneficial impacts, which could transform years of ENSO extremes into the least costly to life and property

Reply

Reply to the comment: Curtis, S., R. F. Adler, G. J. Huffman, G. Gu, D. T. Bolvin, and E. J. Nelkin, 2006: Comments on “El Niño: Catastrophe of opportunity.”. J. Climate, 19, 6439–6442

A framework for the simulation of regional decadal variability for agricultural and other applications

Climate prediction on decadal time scales is currently an active area of research. Although there are indications that predictions from dynamical models may have skill in some regions, assessment of this skill is still underway, and reliable model-based predictions of regional "near-term" climate change, particularly for terrestrial regions, have not yet been demonstrated. Given the absence of such forecasts, synthetic data sequences that capture the statistical properties of observed near-term climate variability have potential value. Incorporation of a climate change component in such sequences can aid in estimating likelihoods for a range of climatic stresses, perhaps lying outside the range of past experience. Such simulations can be used to drive agricultural, hydrological or other application models, enabling resilience testing of adaptation or decision systems. The use of statistically-based methods enables the efficient generation of a large ensemble of synthetic sequences as well as the creation of well-defined probabilistic risk estimates. In this report we discuss procedures for the generation of synthetic climate sequences that incorporate both the statistics of observed variability and expectations regarding future regional climate change. Model fitting and simulation are conditioned by requirements particular to the decadal climate problem. A method for downscaling annualized simulations to the daily time step while preserving both spatial and temporal subannual statistical properties is presented and other possible methods discussed. A "case-study" realization of the proposed framework is described

Improved Combination of Multiple Atmospheric GCM Ensembles for Seasonal Prediction

An improved Bayesian optimal weighting scheme is developed and used to combine six atmospheric general circulation model (GCM) seasonal hindcast ensembles. The approach is based on the prior belief that the forecast probabilities of tercile-category precipitation and near-surface temperature are equal to the climatological ones. The six GCMs are integrated over the 1950–97 period with observed monthly SST prescribed at the lower boundary, with 9–24 ensemble members. The weights of the individual models are determined by maximizing the log likelihood of the combination by season over the integration period. A key ingredient of the scheme is the climatological equal-odds forecast, which is included as one of the "models" in the multimodel combination. Simulation skill is quantified in terms of the cross-validated ranked probability skill score (RPSS) for the three-category probabilistic hindcasts. The individual GCM ensembles, simple poolings of three and six models, and the optimally combined multimodel ensemble are compared. The Bayesian optimal weighting scheme outperforms the pooled ensemble, which in turn outperforms the individual models. In the extratropics, its main benefit is to bring much of the large area of negative-precipitation RPSS values up to near-zero values. The skill of the optimal combination is almost always increased (in the large spatial averages considered) when the number of models in the combination is increased from three to six, regardless of which models are included in the three-model combination. Improvements are made to the original Bayesian scheme of Rajagopalan et al. by reducing the dimensionality of the numerical optimization, averaging across data subsamples, and including spatial smoothing of the likelihood function. These modifications are shown to yield increases in cross-validated RPSS skills. The revised scheme appears to be better suited to combining larger sets of models, and, in the future, it should be possible to include statistical models into the weighted ensemble without fundamental difficulty

Analyzing climate variations at multiple timescales can guide Zika virus response measures

Background: The emergence of Zika virus (ZIKV) in Latin America and the Caribbean in 2014–2016 occurred during a period of severe drought and unusually high temperatures, conditions that have been associated with the 2015–2016 El Niño event, and/or climate change; however, no quantitative assessment has been made to date. Analysis of related flaviviruses transmitted by the same vectors suggests that ZIKV dynamics are sensitive to climate seasonality and longer-term variability and trends. A better understanding of the climate conditions conducive to the 2014–2016 epidemic may permit the development of climate-informed short and long-term strategies for ZIKV prevention and control. Results: Using a novel timescale-decomposition methodology, we demonstrate that the extreme climate anomalies observed in most parts of South America during the current epidemic are not caused exclusively by El Niño or climate change, but by a combination of climate signals acting at multiple timescales. In Brazil, the dry conditions present in 2013–2015 are primarily explained by year-to-year variability superimposed on decadal variability, but with little contribution of long-term trends. In contrast, the warm temperatures of 2014–2015 resulted from the compound effect of climate change, decadal and year-to-year climate variability. Conclusions: ZIKV response strategies made in Brazil during the drought concurrent with the 2015-2016 El Niño event, may require revision in light of the likely return of rainfall associated with the borderline La Niña event expected in 2016–2017. Temperatures are likely to remain warm given the importance of long term and decadal scale climate signals. Keywords: Zika virus Epidemic Climate Climate change Decadal Inter-annual El Niño Brazil Drought Vector contro